Sulcora turns a patient's MRI into an AI-guided planning environment: name a target and it recommends the safest entry, replaces the fixed 2β3 mm margin with a computed, per-structure required standoff that accounts for predicted brain shift and the robot's own targeting error, simulates the insertion, and serves as a validation bench for neurosurgical robotics.
Reaching a deep target means threading a straight line past eloquent cortex, vessels, and tracts. Conventional navigation plans on the preoperative scan, but the brain shifts the moment an instrument enters β so the safety margin you planned isn't the one you get. And the field is racing toward surgical robots with no high-fidelity virtual brain to develop and validate them against. Sulcora is built for both gaps.
Everything below runs today on real imaging β not mock-ups. Try it in the live planner.
Instead of one fixed 2β3 mm margin for every structure, Sulcora computes a per-structure, anisotropic required standoff β inverting a contact-probability model that fuses the robot's targeting-error distribution with the signed, toward-corridor component of predicted tissue shift. A structure predicted to deform toward the corridor demands more margin; one deforming away, less. It now drives the optimizer's ranking, not just a demo.
The margin comes with error bars: deformation-model uncertainty (from an ensemble of displacement fields) and CTβMR registration error are propagated into the same probability inversion, in quadrature β so the standoff widens when the model is less certain, with a worst-case "stays safe even if the robot is 50% worse than spec" view instead of one over-confident number.
Answers the question no planner asks: is THIS robot accurate enough for THIS case? Sulcora computes the maximum targeting error the corridor tolerates under predicted shift, passes/fails a specific device (e.g. "corridor needs β€1.2 mm; a 0.9 mm robot PASSES"), and exports the keep-out geometry it must honor.
Plan the corridor on the CT and pull eloquent cortex and tracts from a co-registered MR β or CTA vessels β into one self-consistent margin, via rigid mutual-information registration across modalities. The real multi-modal case, not one scan at a time.
Load the corridor you drew and Sulcora shows where it agrees, where it recommends safer, and why β the binding structure and the headroom difference. It justifies against your plan instead of silently re-planning.
Name a deep target β including real DBS targets (subthalamic nucleus, globus pallidus) β and Sulcora scores candidate entry corridors against clearance to vessels, eloquent cortex, and tracts at once β optionally against predicted deformation β recommending the safest path with ranked alternatives, under per-structure margin, angle, and depth limits.
Beyond axial/coronal/sagittal β the in-line view (the whole entryβtarget path in one plane) and the probe's-eye view (the cross-section the advancing tip faces): the reformats surgeons actually plan trajectories in.
Real atlases: eloquent cortex (motor, speech, vision), deep structures (thalamus, hippocampus, brainstem), and white-matter tracts (corticospinal, optic radiation). Reports nearest structure, clearance, risk band.
Optionally label eloquent cortex, ventricles, and deep structures directly from the patient's own T1 with a pretrained model (TotalSegmentator / SynthSeg) β registration-free, no atlas warp.
Segment the vasculature from CTA / MR-angiography (multiscale vesselness) and report clearance to the vessel tree β including against the predicted deformed anatomy. Hitting a vessel is the dominant depth-electrode and biopsy risk.
Risk recomputed against the brain's predicted deformed anatomy β the shift conventional static planning ignores.
Predicts probe-induced tissue displacement by solving the elasticity equations on a finite-element mesh (Navier-Cauchy, hexahedral FEM) β upgrading the real-time analytical preview toward the validated model.
Advance the probe entryβtarget and watch clearance to critical structures at every depth, in all views.
Score a simulated robot's targeting accuracy and deformation-aware safety over thousands of runs β before any patient contact.
Plan multiple leads at once β bilateral DBS, multiple SEEG electrodes.
Segment an acute clot from CT, find its long axis, and plan the corridor that maximises clot traversal while clearing vessels and eloquent cortex β the minimally invasive approach shown to improve outcomes in the ENRICH trial (NEJM 2024).
Beyond DBS/SEEG: target any segmented lesion by its centroid (volume in cc) for the safest corridor β biopsy, catheter/laser placement, or tumour access.
One-click PDF for the chart: per trajectory β tri-planar and probe-aligned reformats, a clearance-vs-depth profile, full geometry, and a per-structure clearance table (static and deformation-aware).
Quantitative accuracy metrics (Dice, Hausdorff-95, ASSD, volume & trajectory error) for evidence before clinical validation, and one-click export of the plan to 3D Slicer and a DICOM RT Structure Set for Brainlab / StealthStation.
Head CT is the modality for haemorrhage and trauma β Sulcora loads it in Hounsfield Units with clinical window presets (brain / blood / bone / stroke) and HU-threshold tissue segmentation (skull, brain, CSF/ventricles, acute clot).
Runs on the surgeon's own machine β DICOM (from PACS) or NIfTI opened from disk. The patient image is processed on-device and never leaves it: no cloud upload, no PHI in transit.
One planning engine β the safest corridor to a target while avoiding vessels, eloquent cortex, and white-matter tracts, scored against the brain's predicted shift β generalises across cranial procedures:
Stroke / haemorrhage clot evacuation: CT clot segmentation β long-axis, max-traversal corridor avoiding vessels and eloquent cortex (ENRICH-supported).
Named deep targets (STN, GPi, β¦) β safest entry with ranked alternatives.
Multi-electrode depth trajectories with vessel and structure avoidance.
Lesion-centroid targeting for stereotactic biopsy and laser ablation corridors.
Any segmented lesion (volume in cc) β safest access corridor.
Segment the ventricles, locate the frontal horn, and plan/score a catheter β cannulation, length, and clearance.
Two-compartment segmentation (enhancing + FLAIR infiltration) and extent-of-resection volumetrics with RANO resect grading. Awake-mapping integration in development.
Established navigation platforms are excellent at showing anatomy and tracking instruments. Sulcora adds the things they don't: planning against predicted intraoperative shift, and a virtual brain built for robotics. We integrate with the hardware surgeons already own β we don't replace it.
| Capability | Conventional static planning | Sulcora |
|---|---|---|
| Multiplanar + 3D trajectory planning | β | β |
| Atlas-based eloquent-structure overlay | β | β |
| Automated "name a target β safest entry" | partial / manual | β automated |
| Per-structure direction-dependent required margin (vs fixed 2β3 mm) | fixed margin | β computed |
| Uncertainty-quantified margin (deformation + registration error bars) | β | β |
| Vendor-neutral device qualification (is the robot good enough for this case) | β | β |
| Risk against predicted brain shift | β | β |
| Vessel clearance vs predicted brain shift | β | β |
| Finite-element tissue-deformation model | β | β |
| Robotics development & validation bench | β | β |
| Insertion simulation with depth-wise clearance | limited | β |
Type a structure (e.g., Left Thalamus). Sulcora locates it on the patient's MRI.
One click β the optimizer scores candidate entries against every critical structure at once and snaps to the recommended path, with ranked alternatives and the feasible-corridor count.
Linked crosshairs across axial/coronal/sagittal with a live risk readout β nearest structure, clearance, band, and the deformation-aware value β then simulate the insertion and export the plan report.
Every image is produced by the software itself on the open MNI152 brain with the Harvard-Oxford atlas β generated end-to-end through the codebase, not designed in a graphics tool.














Sulcora is research software today. The route to clinical use is defined, staged, and the basis for collaboration with academic neurosurgery programs.
Tested core, real-MRI planning, structure-aware + deformation-aware risk, finite-element deformation solver, patient-specific segmentation, robotics bench.
Phantom accuracy (TRE), retrospective imaging vs expert plans, validated segmentation; peer-reviewed publication.
Cadaveric validation, validated deformation model, hardware-in-the-loop robotics, QMS / IEC 62304.
510(k)/De Novo pathway, prospective clinical study, integration with navigation hardware.
Includes the application, tested core, sample real MRI, validation/clinical-study plan, and regulatory roadmap.